Experimental validation of a computational fluid dynamics model using micro-particle image velocimetry of the irrigation flow in confluent canals

Aim: This study aimed to experimentally validate a computational fluid dynamics (CFD) model, using micro-particle image velocimetry (micro-PIV) measurements of the irrigation flow velocity field developed in confluent canals during irrigation with a side-vented needle. Methodology: A microchip with confluent canals, manufactured in polydimethylsiloxane was used in a micro-PIV analysis of the irrigation flow using a side-vented needle placed 3 mm from the end of the confluence of the canals. Velocity fields and profiles were recorded for flow rates of 0.017 and 0.1 ml/s and compared with those predicted in CFD numerical simulations (using a finite volume commercial code –FLUENT) for both laminar and turbulent regimes. Results: The overall flow pattern, isovelocity and vector maps as well as velocity profiles showed a close agreement between the micro-PIV experimental and CFD predicted data. No relevant differences were observed between the results obtained with the laminar and turbulent flow models used. Conclusions: Results showed that the laminar CFD modelling is reliable to predict the flow in similar domains.

Description

Tecnologia FCT/MCTES (PIDDAC) and Centro2020, Grant/Award Number: UIDB/04044/2020 and UIDP/04044/2020; Associate Laboratory ARISEand PAMI, Grant/Award Number: LA/P/0112/2020 and ROTEIRO/0328/2013; Research Unit INESC MN and FCT, Grant/Award Number: UID/05367/2020 and UIDB/50022/2020.

Keywords

Computational fluid dynamics Confluent canals Endodontics Irrigation Micro-particle image velocimetry Positive pressure irrigation

URI

http://hdl.handle.net/10400.8/8365

Citation

Rito Pereira M, Silva G, Semiao V, Silverio V, Martins JNR, Pascoal-Faria P, Alves N, Dias JR, Ginjeira A. Experimental validation of a computational fluid dynamics model using micro-particle image velocimetry of the irrigation flow in confluent canals. Int Endod J. 2022 Dec;55(12):1394-1403. doi: 10.1111/iej.13827. Epub 2022 Sep 10. PMID: 36040378.